Improving Methods and Processes for Assessing Codependent Technologies
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The document discusses the value of co-dependent technologies, particularly how diagnostic tests can improve treatment outcomes by identifying suitable patients for drug therapies. It outlines a framework for assessing their value, noting that traditional metrics often fail to capture all benefits, such as societal preferences and quality of life. The authors propose institutional processes and highlight international experiences, particularly in Australia and the UK, to enhance the evaluation of these technologies.
Improving Methods and Processes for Assessing Codependent Technologies
- 1. Assessing the Value of Co-dependent Technologies: How Can Current Methods and Processes Be Improved? ScHARR Seminar, University of Sheffield 16 April 2013 ● Sheffield, UK Martina Garau, Office of Health Economics
- 2. Adrian Towse (OHE) and the other authors of: Garau, M., Towse, A., Garrison, L., Housman, L. and Ossa, D. (2012) Can and should value based pricing be applied to molecular diagnostics? Personalized Medicine. 10(1), 61-72. Acknowledgements
- 3. • What is the value of co-dependent technologies? • Framework for assessing value • How prove value? • How aggregate value dimensions? • Proposed institutional processes • International experience • Australia • NICE • Conclusions Agenda
- 4. • “Technologies that are dependent on another technology either to achieve their intended effect or to enhance their intended effect” (www.health.gov.au) • In particular, a diagnostic test (Dx) can be used to identify patients most likely to: • Respond or fail to respond to a drug treatment (Tx) • Exhibit adverse events But also to: • Monitor responses to drugs • Determine the risk of developing a disease Definition of co-dependent technologies
- 5. 1. Key elements of value are: • Health effects for patients (clinical effectiveness measured by the QALY) • Cost offsets (savings to the health care system) What is the value of co-dependent technologies? (1) Traditionally, ICERs do not capture benefits beyond health attributes NHS,PSS Cost of Treatment A - NHS,PSS Cost of Treatment B ICER = Health effects of Treatment A - Health effects of Treatment B The focus is on downstream effects of treatments not recognising the additional value brought by use of Dx
- 6. • Other value dimensions • Societal preferences giving priority to certain patients or diseases • Quality of life aspects not reflected in generic measures used in CE analyses • Other effects beyond those to patients and NHS (productivity gains) • Health care process related aspects (dignity, time and location of treatment) • Information for the patient independent of health effects What is the value of co-dependent technologies? (2)
- 7. • The value created is a “joint product” and there are no rules for the attribution of the value to one or the other • Garrison and Austin (2007) pointed out that how value is allocated across patients, payers, Dx manufactures and Tx manufacturers depends on the institutional context • E.g. whether the Tx was priced before the Dx was available; the relative strength of intellectual property protection for Dx and Tx • This will have consequences in terms of incentives for evidence generation and subsequent innovation The issue of attribution of value of co-dependent technologies
- 8. Value 1. Reducing drug adverse effects 2. Reducing time delays in selecting optimal Tx 3.Increasing adherence or willingness to start Tx 4. Enabling Tx effective in a small fraction to be made available 5.Reducing uncertainty about value Framework for assessing value of co-dependent technologies Value dimensions derived from: • Characteristics of Dx recently introduced • Literature review on the economics of personalised medicine and value of information of Dx
- 9. Availability of Dx can improve average benefit-risk ratio so, depending on the severity of side effects: • Tx obtains marketing authorisation, or • Use of a licensed Tx in clinical practice increases Example: HLA-B*5701 • Allele associated with hypersensitivity to abacavir for HIV-1 • Identification of the marker has increased prescribing of abacavir, which now is recommended for HLA-B*5701-negative patients in European and US guidelines 1. Reducing or avoiding drug adverse effects
- 10. Identifying non-responders and switching them to an alternative treatment regime/care can: • Improve survival and/or quality of life (particularly in diseases at advanced stages) • Avoid or reduce the cost of treating non-responders • Avoid or reduce inconvenience to patients Example: BCR-ABL • Test identifies chronic myelogenous leukemia (CML) patients who are receiving treatment, but not responding to it • Can prevent the disease from progressing to blast crisis and death, and enables stopping first-line treatment when no longer effective 2. Reducing time delays in selecting optimal Tx
- 11. • Patients are more motivated if they know (ex-ante) the intervention is likely to work • Issue of non-responders who might experience disutility (they can feel “left-behind”) Example: PreDx Diabetes Risk test • Test estimates the patient’s risk for developing Type 2 diabetes over the next five years • This can further encourage patients to follow a healthy lifestyle and take other preventive measures. 3. Increasing adherence or willingness to under- take Tx or other interventions
- 12. A biomarker or other genetic characteristic allowing for patient stratification can: 1. “Rescue” Tx that otherwise may either not have been licensed or have been withdrawn 2. Increase the chance of a Tx meeting reimbursement criteria (if targeting responders improves cost -effectiveness) 3. Accelerate R&D process for Tx (if stratification ascertained at an early development stage) 4. Enabling Tx effective in a small fraction to be made available
- 13. • Gefitinib for non-small-cell lung cancer (NSCLC) initially licensed, but withdrawn when Phase III failed to show a survival benefit. With the identification of EGFR mutations and its association with response rate to TKIs, gefitinib was approved in the EU and other markets in combination with the EGFR mutation test. • NICE recommended trastuzumab for advanced and early-stage breast cancer in HER2/neu positive patients identified with HER2/neu test. The Dx-Tx cost per QALY was found to be below the standard threshold. • Crizotinib targets a small subset of NSCLC patients with an ALK-positive molecular abnormality. The development of the ALK FISH test has accelerated the development process and increased the likelihood of crizotinib delivering health benefits and commercial value. 4. Enabling Tx effective in a small fraction to be made available – Examples
- 14. • Uncertainty around expected health effects and costs; influences the risk of poor value for money for payers • Value of information to patients about their medical condition independent of the health outcome (Ash, et al, 1990) • “Empowerment” (Payne, et al, 2012) • Effect of reassurance (measured with EQ-5D?) (Kenen, 1996) • Lifestyle choices and planning (Lee, et al, 2010) • Example of Oncotype DX ® and MammaPrint ® • Multi-gene assays estimating the risk of recurrence in breast cancer patients following surgery • Can guide intervention decisions and reduce the risk of dispensing unnecessary chemotherapy (reduce resource costs to the healthcare system and adverse effect for the patient) 5. Reducing uncertainty about value
- 15. • Low accuracy of Dx will decrease potential net gains to patients and healthcare system • False positive and false negative patients will not get most appropriate therapy • Tx can be more cost effective when used on its own • When Dx does not provide binary response, depending on the size of the subset for which the Dx does not provide clear-cut result and the Dx cost relative to Tx • When Dx has low accuracy Other factors affecting value of co-dependent technologies
- 16. • Barriers to evidence generation • Cost and feasibility of certain study designs • Protection of intellectual property rights of Dx • Regulatory processes for diagnostics • Assessment of competitive tests with similar clinical use How prove value of co-dependent technologies?
- 17. How is value aggregated? Key issues Key merits Net benefit As the sum of the benefits, each assessed in monetary terms Challenges estimating the value in monetary terms of each type of value Allocating a monetary value to health has been always one of the mayor criticisms Arguably, a better grounding in economic theory Facilitates the comparison of value and value for money across health and other sectors Use of monetary value may resonate better with some (private) payers MCDA As the sum of the points assigned to each aspect of value The cost -effectiveness threshold would need to be re-assessed in terms of the cost per incremental “point” A pragmatic approach, widely used in the UK public sector. A more transparent (compared to a weighted QALY, or deliberative process alone) means of addressing multiple criteria MCDA is used in local NHS commissioning – potential to develop a consistent priority-setting framework for both new and existing health care technologies Weighted adjusted QALYs 1. By QALYs gained, up-rated or down-rated by one or multiple weights to represent the magnitudes of other aspects of value; or 2. Direct estimation of how people trade off QALY gains with other value elements Assumes that all other sources of value are proportional to the number of QALYs gained. Implications for the threshold. If the value of new technologies is assessed in terms of a range of criteria, then opportunity cost also must be considered in the same terms, not just QALYs foregone. Even if a simple social weighting or QALYs is applied, opportunity cost will change Is it relevant to state here the classic arguments in favour of the QALY such as: - Allows for comparisons across therapeutic areas in the NHS - “A QALY is a QALY” argument - Well established in the UK within HTA bodies (and academic centres) - Understood by health economics community Deliberative process Weights are assigned by a committee to each relevant aspect of value The weights are often implicit Are implications for the threshold Provides an element of flexibility Is a well-recognised approach taken by HTA bodies around the world. How aggregate value dimensions? Source: adapted from Sussex, et al, 2013
- 18. • A joint Dx-Tx review of “at launch” technologies; to be done by a drug committee to exploit synergies across Dx and Tx • However, there is a need to address the lack of expertise of most drug committees in the Dx area • A separate Dx committee to develop Dx-specific expertise and to assess multiple tests with similar clinical use • However, there may be a trade-off if there are not enough decisions to justify a distinct committee • A comprehensive and consistent approach to assessing value of both Dx and Tx Proposed institutional processes for co-dependent technologies
- 19. Proposed institutional processes New Dx Dx linked to a Tx (companion Dx) Dx-Tx pair launched simultaneously Dx-Tx joint assessment via Drug process Single Dx launched separately Dx assessed via Diagnostic- dedicated process Multiple Dx with same clinical use Dx assessed via Diagnostic- dedicated process Dx not linked to a Tx Dx assessed via Diagnostic- dedicated process
- 20. • Until recently, Dx and associated Tx assessed via different committees (MSAC and PBAC) • No clear structure for consideration of the interactions and benefits from joint use • New coordinated process and decision framework for “co- dependent technologies” • “Integrated” applications combining information from Dx and Tx manufacturers • Reimbursement decisions are made jointly by PBAC and MSAC to ensure optimal clinical use (Merlin, et al, 2012) • The preferred type of evidence to show clinical benefit is a randomised clinical trial International experience: Australia
- 21. • NICE has dedicated-process for stand-alone Dx that follows very closely that used for drugs • Strong preference for measuring health gains with the QALY • Value dimensions beyond health effects, such as value of information to patients and process-related benefits, are not explicitly factored in • “At launch” combinations are appraised via the drug review programme (TAs) • No explicit consideration of test-related parameters (accuracy, costs) • Value dimensions beyond health effects are not explicitly factored in International experience: NICE in England and Wales
- 22. • The use of Dx-Tx combinations can deliver health gains and cost savings within the health care system, but also generate broader benefits to patients and society • To ensure efficient use of limited resources, health decision makers should take account of the full value generated by health technologies • Clear incentives are needed to encourage evidence collection • HTA and other decision making systems need coordinated and consistent approach to assessing value of Dx and Tx • NICE is heading in this direction, but does not yet have a comprehensive approach to assessing the value of Dx or Tx • In Australia, the common methodology needs to be supported by a realistic view of evidence development Conclusions
- 23. Ash, D.A., Patton, J.P. and Hershey, J.C. (1990) Knowing for the sake of knowing: The value of prognostic information. Medical Decision Making. 10(1), 47-57. Garau, M., Towse, A., Garrison, L., Housman, L. and Ossa, D. (2012) Can and should value based pricing be applied to molecular diagnostics? Personalized Medicine. 10(1), 61-72. Garrison, L.P. and Austin, M.J.F. (2007) The economics of personalized medicine: A model of incentives for value creation and capture. Drug Information Journal. 41(1), 501-509. Lee, D.W., Neumann, P.J. and Rizzo, J.A. (2010) Understanding the medical and nonmedical value of diagnostics testing. Value in Health. 13(2), 310-314. Merlin, T., Farah, C., Schubert, C., Mitchell, A., Hiller, J.E. and Ryan, P. (2012) Assessing personalized medicines in Australia: A national framework for reviewing codependent technologies. Medical Decision Making. 33(3), 333-342. Kenen, R.H. (1996) The at-risk health status and technology: A diagnostic invitation and the gift of knowing. Social Science & Medicine. 42(11), 1545-1553. Payne, K., McAllister, M. and Davies L. (2012) Valuing the economic benefits of complex interventions: When maximising health is not sufficient. Health Economics. 22(3), 258-271. Sussex, J., Towse, A. and Devlin, N. (2013) Operationalising value based pricing of medicines: A taxonomy of approaches. Pharmacoeconomics. 13(1), 1-10. References
- 24. To enquire about additional information and analyses, please contact Martina Garau: mgarau@ohe.org To keep up with the latest news and research, subscribe to our blog, OHE News. Follow us on Twitter @OHENews, LinkedIn and SlideShare. Office of Health Economics (OHE) Southside, 7th Floor 105 Victoria Street London SW1E 6QT United Kingdom +44 20 7747 8850 www.ohe.org OHE’s publications may be downloaded free of charge for registered users of its website. ©2013 OHE
Editor's Notes
- #9: key pathways of value that the use of Dx to inform treatment or intervention decisions
- #10: Dx can be available to select patients that are more or less likely to develop adverse effects a. allow a treatment to receive marketing authorisation by improving the benefit-risk ratio associated with the treatment b. increase adoption of the treatment, in cases where a treatment is licensed, but is not widely used because of its perceived unfavourable average benefit-risk balance when considered across a broad patient population
- #11: Avoid trial and error approach and identify the most suitable intervention First two points captured in CE analysis; last one notI will illustrate those points using the exampleit avoids or reduces inconvenience to patients who do not need to experience a long diagnostic process or try different therapies to identify the one most suitable
- #12: Patients are more motivated if they know the intervention is likely to work. In the case of companion diagnostics, however, patients found to be non-responders might experience disutility as they can feel ‘left-behind’, and lose hope and even motivation to pursue any other, less effective, but appropriate therapy.
- #13: Dx to stratify patientsThree cases where Dx have a positive impact and it is introduced in the market at different stages of the Tx lifecyclepatient stratification in oncology clinical trials could reduce attrition rates in overall clinical development and, in particular, attrition rates from Phase II to Phase III
- #15: Measured by EQ5D but may not be captured as patients focus on Tx effects rather than on the overall experience of Dx-Tx
- #16: Those were the five pathways through which co-dependent technologies such as Dx and Tx can generate value as compared to a situation where the intervention is used on its own
- #19: Only third poikey issue for the assessment of co-dependent technologies is that they are perceived as a joint product so there is no an approach to allocate the value brought by each part.) I then discussed a framework identifying how Dx can bring additional value to Dx-Tx pairs. Here I discuss briefly which the type of process can help ensuring those elements are assessed and considered in the HTA or P&R system. nt
- #21: Guide for submission sets a high standard of evidence to demonstrate impact of the test on patient outcomesWhich raises important questions as to how value should be demonstrated and who can generate the evidence